In our experimental conditions, we are[unreadable] determining "colocalization" in the resolution limit of light microscopy. In this PPG, the "colocalization" term[unreadable] will imply that two molecules may share the same microenvironment and/or can be elements of the same set[unreadable] of macromolecular complexes which are resolved by the optical system. Forexample, two proteins forming part[unreadable] of two different molecular complexes may show positive "colocalization" tests due to the lack of the optical resolution[unreadable] to detect each molecular complex. Still, the positive test will indicate that the two proteins are located proximally[unreadable] within the optical resolution. Thus, in "colocalization" determinations it is critical to achieve the maximal x,y,z[unreadable] resolution. In this PPG, we will use developed methodologies in the laboratory to achieve the highest resolution[unreadable] attainable with optical microscopy. We have maximized the optical linearity and the quantity of light recorded by the[unreadable] detectors in conjunction with image restoration analysis to recover the light lost by the optical imperfections of the[unreadable] system. This methodology has allowed us to reach a resolution in the x,y plane of about 100-200 nm (see Figs. 1-3).[unreadable] Definition of "colocalization" and random "colocalization". In a cell labeled with two fluorophores, one red (R)[unreadable] and one green (G), the number of voxels above threshold that are labeled with the red fluorophore is nRand the[unreadable] number labeled with the green fluorophore is nG. The number of colocalized voxels, i.e. those voxels containing[unreadable] intensity signals above threshold from both fluorophores is ncoioc. The percentage of "colocalization" of G with R is[unreadable] given by 100*^^- (eq. 1); and the percentage of "colocalization" of R with G is given by 100*^^ (eq. 2). In[unreadable] n* nc[unreadable] these measurements we will estimate the probability that the measured "colocalization" could occur by chance by[unreadable] calculating % random "colocalization" from ((area_1x area_2)/total areaA2) x100, where area_1 and area_2 are the[unreadable] areas above threshold (eq. 3).[unreadable] The "colocalization" method is generally based on the user ability to set the intensity threshold, and has the major[unreadable] draw back of the lack of a mathematical model to adjust the threshold of both images that will define the degree of[unreadable] paired pixel overlap. Most importantly the pixel overlap method has the limitation of being a binary test that[unreadable] determines whether the two paired pixels in two images have or not intensities above the intensity threshold and does[unreadable] not consider whether the two stained proteins have a landscape of intensity staining with a high degree of correlation,[unreadable] as it would be expected if they are elements of a common complex. The application of a correlation measurement[unreadable] was recently described by Li et al. (2004)7.[unreadable] In the following sections, I will discuss and apply developed algorithms to quantify the degree of protein-protein[unreadable] association by two methods, the INTENSITY CORRELATION ANALYSIS and the INTENSITY THRESHOLD[unreadable] "COLOCALIZATION"ANALYSIS.[unreadable] Intensity correlation analysis. In the present analysis, we acquire high resolution images to compare the[unreadable] correlation of pixel intensities in equivalent x,y coordinates of paired images from cells double stained for two different[unreadable] proteins. The prediction is that if two proteins are elements of the same macromolecular complex, the intensity[unreadable] staining landscape of the two images should have a x,y pixel to pixel positive correlation. On the contrary, if the two[unreadable] proteins are localized in distinct compartments the result will be a negative correlation. Finally, if the proteins in the[unreadable] two images are labeled in a diffuse non structured pattern (random), the correlation will tend to 0.[unreadable] This method is based on the principle that for any set of values the sum of the differences from the mean equal zero,[unreadable] i.e., ?N(A-a)=0, where a is the mean of the distribution with N values of Al. In the experiment N is the number of[unreadable] pixels, and A is the intensity for each pixel. If we have two set of values in two arrays 1 and 2 with N pixels per array[unreadable] having a random distribution of intensities A-,and 6, for arrays 1 and 2 , the sum of the product of their differences will[unreadable] also tend to zero, thus IN(A-a)(S,-Jb)~0. On the other hand, if the two intensities are positively correlated, the product[unreadable] will tend to be a positive value (^(Ara)(Brb}>G) and if they are negatively correlated the product will tend to a[unreadable] negative value (LN(Ara)(Brb)<0). To perform the analysis, we generated two intensity arrays 1 and 2 of equivalent[unreadable] x,y coordinates of the paired images. The pixel intensity values of the arrays were normalized to their individual[unreadable] maximum and a 3rd array was obtained from (Ara)(Brb). Rows with 0 values with the same x,y coordinates in both[unreadable] PHS 398/2590 (Rev. 05/01) Page 382 Continuation Format Page[unreadable] CONTINUATION PAGE Principal Investigator/Program Director (Last, first, middle): Ping, Peipei (Stefani, Heart Biology Core)[unreadable] images were eliminated. We generated the correlation plot between the first two arrays and between array 3, and[unreadable] arrays 1 and 2. As indicated, positive, negative or 0 values of EN(A-a)(S,-6) would be an indication of positive,[unreadable] negative, and 0 correlation. To evaluate the statistical[unreadable] significance of the data we used the non-parametric[unreadable] Psign test by counting the number of positive N+ and[unreadable] negative N .numbers of the 3rd array (Ara)(Brb). We[unreadable] generated the intensity correlation quotient (ICQ) from[unreadable] N+/(N+-N.)-0.5. Values of ICQ are 0> for positive[unreadable] correlation, <0 for negative correlation and - 0 for[unreadable] random correlation. The correlation coefficient r[unreadable] (COR) between arrays 1 and 2 was calculated from[unreadable] r=2xy/(NSxSy) wher r = correlation coefficient, xy =[unreadable] product of deviation scores, N = sample size, Sx =[unreadable] standard deviation of X (intensities in first image), and[unreadable] Sy = standard deviation of Y (intensities in second[unreadable] image).[unreadable] We initially performed intensity correlation analysis[unreadable] using as an experimental "colocalization" model the[unreadable] same protein tagged by two different antibodies at[unreadable] different sites. Confocal images were at 0.038[unreadable] urn/pixel in the x, y axis, and every 0.1 urn in the z[unreadable] plane. We used as the target protein caveolins (1-3).[unreadable] Caveolin 3 (CAV-3) was immunostained with anti-[unreadable] CAV3 antibody and all the caveolins with a generic[unreadable] anti-caveolin antibody (anti-CAVg). Since in the heart,[unreadable] CAV3 is the most abundant caveolin, anti-CAVg[unreadable] should mainly stain CAV3. As a model for non-[unreadable] "colocalization" (segregated localization), we[unreadable] displaced by 4 pixels the x,y plane of the anti-CAVg[unreadable] labeled image. In Figure 6, panels A,B are single[unreadable] confocal sections of cardiomyocytes immunostained[unreadable] for CAV3 and caveolin generic CAVg, respectively.[unreadable] Panels C and D are the regions marked with squares[unreadable] in (A) and (B) at higher magnification with the[unreadable] corresponding overlay in E. Note the strikingly similar Fig. 6. Intensity correlation analysis using "colocalization" and non[unreadable] pattern of pixel intensity distribution for CAV3 (C) and "colocalization" experimental models. (A,B) Single confocal sections[unreadable] CAVg (D) that generates the yellow signal in E. In of cardiomyocytes immunostained for CAV3 (A) and caveolin generic[unreadable] CAVg (B). (C, D) Regions depicted in (A) and (B) at higher[unreadable] contrast, when the region D was displaced by 4 pixels[unreadable] magnification with their overlay (E). (F) overlay of (C) and (D) but with[unreadable] in the x,y plane, the overlay shows a clear separation[unreadable] the region D displaced 4 pixels in the x,y plane. (E1-E3) Intensity[unreadable] between green and red signals (F). correlation analysis for (C, D). (E1) Correlation plot of CAV-g vs.[unreadable] CAV3. (E2.E3) Plots of CAV-g and CAV3 vs. (A,-a)(Brb). (F1-F3)[unreadable] In the same figure, graphs E1-E3 illustrate the[unreadable] Intensity correlation analysis of F. (D) (CAVg) was displaced by 4[unreadable] intensity correlation analysis for (C) and (D). E1 is the pixels in the x,y plane; the analysis shows a negative correlation. (G1-[unreadable] correlation plot of CAV-g vs. CAV3 pixel intensities. G3) Intensity correlation analysis of two arrays with random numbers.[unreadable] The points are not randomly distributed (compare with[unreadable] G1 for a random plot) with a correlation coefficient of 0.84. E2 and E3 are the plots of CAV-g and CAV3 vs. (Ara)(Br[unreadable] b). As expected for a positive correlation, the majority of the (A,-a)(Brb) have positive values (red dotted line marks[unreadable] the zero value of the (Ara)(Brb) axis). The ICQ was 0.36 with Psign test<0.001. We can conclude from these data[unreadable] analysis that the pixel intensities are highly correlated as expected from the dual labeling of the same protein. Graphs[unreadable] F1-F3 show an equivalent analysis for the pair of images generating the F overlay. As indicated, in this case the[unreadable] region D (CAVg) was displaced by 4 pixels (0.038 um/pixel) in the x,y plane. As result of the displacement, the[unreadable] analysis should give a negative correlation. In fact, the correlation coefficient of the plot in F1 was -0.24 and ICQ -0.15[unreadable] with PSign <0.005. Graphs G1-G3 show the results of a random distribution. In this case, the data arrays were made[unreadable] with random numbers. G1 shows the random distribution of pixel intensity in the correlation and G2, G3 quasi equal[unreadable] number of positive and negative (Ara)(B,-b) values.[unreadable] PHS 398/2590 (Rev. 05/01) Continuation Format Page[unreadable] CONTINUATION PAGE Principal Investigator/Program Director (Last, first, middle): Ping, Peipei (Stefani, Heart Biology Core)[unreadable] Intensity threshold "colocalization". The most difficult task to measure "colocalization" by changing the intensity[unreadable] threshold in both images is to establish criteria of setting the intensity threshold values which define the areas above[unreadable] threshold to be compared. The intensity threshold should eliminate non-specific signals as detector noise, non-[unreadable] specific antibody binding, autofluorescence, and blur[unreadable] from optical imperfections. In "cotocalization" studies.[unreadable] 100 D CAVg OVER CAV3[unreadable] the intensity threshold is typically set in an arbitrary[unreadable] manner bv the user and "colocalization"[unreadable] measurements are questionable. To overcome this[unreadable] caveat, we have developed mathematical criteria to[unreadable] set the threshold levels. Most importantly, in the[unreadable] same set of paired images, curves are constructed to u[unreadable] measure "colocalization" level as function of the 3[unreadable] intensity thresholds selected. We found convenient to[unreadable] plot % "colocalization" level vs. random % 8[unreadable] OCAVgOVERCAV3[unreadable] "colocalization" (calculated from eq. 2 taking into CAV3OVERCAVg[unreadable] account the total area and the two sets of thresholded (4 Pixel shift)[unreadable] areas), using various threshold levels in paired[unreadable] images. In this manner, we can have an estimate of 100.00 10.00 1.00 0.10[unreadable] the level of contamination random "colocalization". in % RANDOM COLOCALIZATION[unreadable] the "colocalization" measurements, (see below). We[unreadable] have calculated intensity threshold values in two Fig. 7. Threshold intensity "colocalization" analysis as function oi[unreadable] different manners: METHOD 1. Varying the intensity threshold intensity. % "colocalization"vs. % random "cotocalization" is[unreadable] threshold as a function of the average intensity. plotted for random "colocalization" (r), CAVg over CAV3 (a),and[unreadable] CAV3 over CAVg (b) (for panels C,D, Fig. 6), and CAVg over CAV3[unreadable] independently in both images, and in a sequential[unreadable] (c), and CAV3 over CAVg (d) (overlapped images, Fig. 6F). The[unreadable] manner, and METHOD 2. Adjusting the threshold[unreadable] CAVg image in panel F was 4 pixel x,y shifted. Note the high CAVg[unreadable] intensities of both images to attain the same area over CAV3 of ca. 85% at 1 % random "colocalization". The 4 units x,y[unreadable] (number of pixels) above threshold in both images. In pixel shift dramatically reduced the "colocalization" level.[unreadable] METHOD 1 the threshold in each image is first Calibration=0.038 urn/pixel.[unreadable] calculated from the average intensity of all the pixels in each image, thereafter the same procedure is applied in the[unreadable] resulting images and so on. In this manner, a set of images are generated where the threshold values are the[unreadable] average intensities of the respective previous images. This method has the advantage that all the pixel intensity[unreadable] values are considered to calculate the threshold values, reducing asymmetries and differences of the total point[unreadable] histogram between both images. In METHOD 2, intensity threshold values are calculated in each image with the[unreadable] constrain that the number of pixels above threshold (thresholded area) is the same in both images and areas in[unreadable] decrementing steps from 100% to ca. 1% of the total area are calculated. In a given area having an identical value[unreadable] for both images, one subset of pixels in the same x, y coordinates will have intensities in both images while a second[unreadable] set of pixels will have intensity values only in one image. The "percent colocalization" is calculated from the ratio of[unreadable] the number of pixels with intensities (>0)in both images with the number of pixels with intensities (>0)in one image.[unreadable] In this algorithm, the colocalization level of Image A over Image and of Image A over A is the same. Minor[unreadable] differences can arise from the computational limitations to define the right threshold to obtain two identical areas in[unreadable] both images. As in METHOD 1,"%colocalization" will be plotted vs."% random colocalization". These two methods[unreadable] are testing pixel overlap in a binary manner at various threshold intensities and thresholded areas. Therefore, this[unreadable] analysis is also taking into consideration the degree of correlation of the landscape of intensity staining in both[unreadable] images.[unreadable] Figure 7 illustrates METHOD 1 by evaluating the "colocalization" level as function of the threshold intensity[unreadable] determined sequentially by the average intensity of the individual images. The graph illustrates the relationship[unreadable] between percent "colocalization" and percent random "colocalization". As previously discussed, a positive[unreadable] "colocalization" has to be much higher than the random "colocalization". Plots (a and b) are the CAVg over CAV3 and[unreadable] CAV3 over CAVg "colocalization" levels, respectively. Plot (r) is the theoretical percent random "colocalization". It is[unreadable] clear that at 1% random "colocalization", the percent "colocalization" of CAVg over CAV3, and CAV3 over CAVg is[unreadable] much higher being 85% and 64%,respectively. Plots (c and d) correspond to panel F of Fig.6, where 4 pixel units[unreadable] (0.038 urn/pixel) were shifted in the x,y plane. As for the intensity correlation analysis, the pixel shift practically[unreadable] eliminated the "colocalization" level between CAVg over CAV3 and vice versa. Identical results were obtained by[unreadable] using METHOD 2 (not shown). Equivalent results are illustrated with the three methods in Fig. 16, showing their[unreadable] application to investigate '"'colocalization'" of cytochrome C-GFP fusion protein expressed in cardiomyocytes via[unreadable] adenovirus and a mitochondria marker (mitotracker TMRE).[unreadable] PHS 398/2590 (Rev.05/01) Continuation Format Page[unreadable] CONTINUATION PAGE Principal Investigator/Program Director (Last, first, middle): Ping, Peipei (Stefan!, Heart Biology Core)[unreadable] Figure 8 shows another example of "colocalization" measurement varying the threshold as a function of the average[unreadable] intensity (METHOD 1) by comparing the "colocalization" level of a transfected labeled protein with a native[unreadable] immunolabeled protein. Confocal images were at 0.054 urn/pixel in the x, y axis, and every 0.1 urn in the z plane.[unreadable] Panels A, B and C are single confocal sections of a cultured neonatal rabbit myocyte transfected with p38-GFP fusion[unreadable] protein (green) and immunostained with anti-TAB1 antibody (red). Regions 1 and 2 are displayed at a higher gain in[unreadable] D,E and H,l, respectively. Note the distribution outline in defined clusters of TAB1 and P38 which has a filamentous[unreadable] pattern in the cytoplasm. Graphs (G) and (K) show the relationship between percent "colocalization" and percent[unreadable] random "colocalization" for regions 1 and 2, respectively.[unreadable] In the graph, each data point was obtained by[unreadable] sequentially setting the threshold as function of the[unreadable] average intensity. The percent "colocalization" and[unreadable] percent random "colocalization"was calculated from eqs.[unreadable] 1-3. The theoretical curve of random "colocalization"was[unreadable] plotted as a reference (labeled RANDOM). With a .RcgioncV c ? Region 1 I |_| Region 2 | Region2[unreadable] minimal threshold value, the area above threshold equals[unreadable] the total area of the region; thus, the values of percent[unreadable] "colocalization" and percent random "colocalization" are[unreadable] 100. As the intensity threshold values are increased as a[unreadable] function of the average intensity, the thresholded areas[unreadable] are reduced resulting in diminished "colocalization". The[unreadable] arrow marks the "colocalization" level when the percent[unreadable] random "colocalization" is 3% (see table at the right[unreadable] bottom). Panels F and J show the corresponding masked XUM1K00DOM1C0OLOC1A.U0ZATO0H.1[unreadable] DCAV3 OVER UVg[unreadable] thresholded areas (yellow shows the overlap). The ? NCX OVER CAVg COLOCALIZATION ANALYSIS (Arrow in G and K)[unreadable] "colocalization" level (40-50%) is much higher than the CYTOPLASM (Region 1) NUCLEUS(Region Z)[unreadable] Total Area = 131 \rn\2 Total Area = 104 (im2[unreadable] expected random "colocalization"for those areas. These P38Area =25 prn2 P38Area =20 ym2[unreadable] TAB1Area =21 pm2 TAB1 Area = 20 Mm2[unreadable] determinations indicate a high level of "colocalization" in P3B Over TAB1 =41% P38 Over TAB1= 50%[unreadable] the nucleus and cytoplasm for TAB1 and P38. Similar <-> 100%.0RAN1D0.O0 M CO1L.0OCALIZ0A.1TION 0.01 TRAanBd1omOvCeorloPc3.8==35.0% RTaAndBo1mOCvoelor cP.3=8=3.502%[unreadable] values were obtained using METHOD 2. Panel L shows Fig. 8. "Colocalization" measurements in neonatal heart cells[unreadable] examples of minimal and maximal "colocalization"in adult transfected with p38GFP (green) and immunolabeled with anti-[unreadable] heart myocytes. Minimal "colocalization" was observed TAB19 antibody (red} (seetexO. _[unreadable] between caveolin generic (CAVg) and Na/Ca2+ exchanger (NCX). Note that the curve for NCX over CAVg follows[unreadable] closely the theoretical curve for random "colocalization". In contrast, maximum "colocalization" was obtained when[unreadable] the same protein (caveolin) was immunostained with monoclonal (CAV3) and polvclonal (CAVg) antibodies. In the[unreadable] latter condition, one should expect 100%"colocalization" if the antibodies would have 100%efficiency to stain the[unreadable] target protein. Evidently, this is not the case, and only a fraction of caveolin can be simultaneously stained by both[unreadable] antibodies.[unreadable] Evaluation of intensity correlation and intensity threshold analyses. The major advancementin our studies[unreadable] is the achievement of high spatial x.v.z resolution bv image restoration in conjunction with the use of[unreadable] analytical user-independent colocalization methods (intensity correlation analysis and intensity threshold[unreadable] colocalization). These methods will provide a strong argument to define whether two proteins are elements or not of[unreadable] the same macromolecular complex, and,in particular for this PPG, to quantify with rigorous algorithms protein-protein[unreadable] "colocalization" and association of proteins with mitochondria. Both "colocalization" methods, intensity correlation[unreadable] analysis and intensity threshold "colocalization" have advantages and drawbacks. The intensity correlation is a[unreadable] powerful statistical method that evaluates the correlation between the landscapes of paired images. One draw back[unreadable] is that not clearly interpretable results are obtained if the images have regions of positive and negative correlation. To[unreadable] circumvent this deficiency the user should evaluate different cytoplasmic and membrane regions. Another drawback[unreadable] of this analysis is that it does not give the "colocalization" level as the overlap factor as in the intensity threshold[unreadable] analysis. On the other hand, the major draw back of the intensity threshold method is the mathematical definition of[unreadable] random "colocalization" due to the lack of a valid estimate of the total area where the protein can be distributed[unreadable] randomly. This is not the case for cytosolic proteins that can randomly distribute in the cytoplasm and the total area is[unreadable] directly measured from the region of interest. In general, proteins are targeted to regions making very difficult to[unreadable] measure the surface of the targeted area. For example, the total area that one has to consider for membrane proteins[unreadable] is difficult to estimate. Nevertheless, the fact that when positive, "colocalization" gives a "co/oca//zaton"/tandom[unreadable] "colocalization" ratio of several orders of magnitude higher which makes it easy to define the "colocalization" level. In[unreadable] fact, if "colocalization" remains significant in the limit of random "colocalization" (-0.1%), it is a strong argument of a[unreadable] PHS 398/2590 (Rev. 05/01} Continuation Format Page[unreadable] CONTINUATION PAGE Principal Investigator/Program Director (Last,first,middle): Ping, Peipei (Stefani, Heart Biology Core)[unreadable] positive finding. Moreover, as control. I have been able to detect lack of "colocalization" of two membrane proteins[unreadable] that do not co-immunoprecipitate such as NCX and caveolin as shown in Fig.8L. In view of these considerations,[unreadable] one of the major advantages of the intensity correlation analysis is that it is not dependent on the estimate of random[unreadable] "colocalization". Thus, by using both types of analysis. I am confident that reliable "colocalization" measurements will[unreadable] be obtained in the limit of the microscope spatial resolution. The implementation of the intensity correlation analysis is[unreadable] very laborious and time consuming. For example, the analysis of 1024x1024 pixel image takes about 6 hrs.At[unreadable] present, I am developing a program to increase the processing speed and to automatically analyze various[unreadable] predetermined regions in the image. Alternatively, I am also developing a program that will determine the degree of[unreadable] correlation from the x and y first derivative of the landscape. This approach may increase the sensitivity and[unreadable] discrimination of the analysis, since it will take also into account the slopes of the valleys in the landscape.[unreadable]